The genetic architecture of multimodal human brain age

The complex biological mechanisms underlying human brain aging remain incompletely understood. This study investigated the genetic architecture of three brain age gaps (BAG) derived from gray matter volume (GM-BAG), white matter microstructure (WM-BAG), and functional connectivity (FC-BAG). We identified sixteen genomic loci that reached genome-wide significance (P-value < 5×10−8). A gene-drug-disease network highlighted genes linked to GM-BAG for treating neurodegenerative and neuropsychiatric disorders and WM-BAG genes for cancer therapy. GM-BAG displayed the most pronounced heritability enrichment in genetic variants within conserved regions. Oligodendrocytes and astrocytes, but not neurons, exhibited notable heritability enrichment in WM and FC-BAG, respectively. Mendelian randomization identified potential causal effects of several chronic diseases on brain aging, such as type 2 diabetes on GM-BAG and AD on WM-BAG. Our results provide insights into the genetics of human brain aging, with clinical implications for potential lifestyle and therapeutic interventions. All results are publicly available at https://labs.loni.usc.edu/medicine.


European ancestry populations
We performed seven sensitivity check analyses to scrutinize the robustness of our primary GWASs on European ancestry populations.

Machine learning model-specific GWAS
We used GM-BAG to demonstrate this sensitivity check by comparing i) SVR using MUSE ROIs and ii) CNN using voxel images 2 (GWAS summary statistics shared by the authors) to our main results obtained from Lasso using MUSE ROIs.

P-value:
When comparing the SVR using MUSE ROIs (as replication, MAE=4.43 years) to Lasso using MUSE ROIs (as discovery, MAE=4.39 years), we found a 100% concordance rate of the SNPs identified for the GM-BAG GWAS.The BAGs derived from the two machine learning models were highly correlated (r=0.99;P-value<1x10 -10 ).

β value:
When comparing the SVR using MUSE ROIs to Lasso using MUSE ROIs (as discovery), we found that the 3382 significantly replicated SNP (P-value<0.05)showed the same sign of β values from the linear regression models (Pearson's r=1; P-value<1x10 -10 ).

P-value:
We finally found a 92.43% concordance rate of the SNPs identified in the GM-BAG GWAS using the 119 MUSE ROIs 3 (as discovery, MAE=4.39 years) and voxel-wide RAVENS 4 maps (as replication, P-value < 0.05/3382, MAE=5.12 years).The BAGs derived from the two types of features were significantly correlated (r=0.74;P-value<1x10 -10 ).The brain age prediction performance using RAVENS showed marginal overfitting, with an MAE of 4.31 years in the training/validation/test dataset and an MAE of 5.12 years in the independent test dataset.

P-value:
We evaluated the generalizability of the GM-BAG GWAS findings from the UKBB dataset to the ADNI whole-genome sequencing (WGS) data.When considering the concordance rate based on P-values, we observed a high concordance rate (83.57%) for the GWASs performed using the ADNI WGS data (N=1104) as a replication dataset (N=2583 out of 3091; 291 SNPs missing from the ADNI data) using a nominal P-value threshold.No SNPs survived the Bonferroni correction.

β value:
However, it's noteworthy that the β values of these significant SNPs exhibited a significant correlation (r=0.83;P-value<1x10 -10 ) between the two datasets.This observation underscores the importance of collecting genetic data within specific disease populations and throughout the entire lifespan (Supplementary Figure 7 and eFile 7).
3) Sensitivity checks of causal effects of AD on WM-BAG.A) Scatter plot for the heterogeneity of the causal effects.B) Funnel plot shows no apparent asymmetry of the causal effects.C) Single-SNP MR results.D) Leave-one-out analyses.Each dot represents the mean value of the estimated parameters, and the error bar displays its 95% confidence interval (C, D) or standard errors of the parameters (A).

Supplementary table 1: Brain age prediction performance using GM, WM, and FC-IDP.
We reported each machine learning model's mean absolute errors (MAE, year) and Pearson's correlation coefficient (r).The cross-validated (CV Test) and independent (Ind.Test) testing results were shown.

Table
A shows the results from the CV Test and the independent (Ind.)Testperformance.TableBcontains the results of the sex-stratified experiments. A:

Brain age prediction results from the CV and independent test dataset.
The bolded text represents the lowest MAE for IDPs from each MRI modality.For WM-IDP, we fit the models with different combinations of features: i) 108 weighted mean TBSS WM-IDP from FA, MD, OD, and NDI; ii) 192 skeleton mean values of WM-IDP from FA, MD, OD, and NDI; iii) 48 FA WM-IDP. B: